5 Key Black Box Testing Techniques with Example

In April 2024, a software program designed for social media interaction mistakenly accused an NBA player of vandalism due to a misinterpretation of posts. This incident highlights the critical need for rigorous testing of software systems to prevent such errors.

Without proper software testing, how can we truly trust any software system? Black box testing plays a crucial role in evaluating a system's performance from a user's perspective, identifying potential issues that might go unnoticed otherwise.

What are the various types of software systems?

• Data Management Systems: Systems that handle data storage, retrieval, and manipulation, such as databases or file systems.
  
• Communication Systems: Systems that facilitate communication between users or devices, such as messaging apps or email clients.
  
• Multimedia Systems: Systems that manage multimedia content, such as audio players or video streaming apps.
  
• Utility Systems: Systems that provide utility functions, such as calculators or weather apps.

What is black box testing in software testing?

The software system is treated as a "black box" in black box testing, with no internal workings examined. We analyze the system's response to different stimuli by concentrating on its inputs and outputs.

Compared to testing typical software, testing complex software systems has different challenges. They are prone to unanticipated mistakes because of their dynamic nature and capacity for learning over time. Black box testing helps address these challenges by focusing on real-world scenarios and user interactions.

The 5 key types of black box testing techniques

In this section, we'll explore five key black box techniques and demonstrate how they can be applied to test specific functions of a software.

Equivalence class partitioning

ECP involves dividing potential inputs into categories (equivalence classes) based on their expected behavior. This helps us ensure the software system responds consistently within each class. Imagine testing a spam filter. We might have classes for:

• Valid emails (e.g., personal emails, newsletters)
• Spam emails (e.g., promotional offers, phishing attempts)
• Borderline cases (e.g., emails with unusual formatting, legitimate emails with promotional content)
  

For a black box testing example, you're testing an AI-powered recommendation system for a music streaming service. The system suggests songs based on user preferences, such as genre and mood.

Test Cases:

1. Classify user preferences into equivalence classes:some text
   • Class 1: Pop music lovers
   • Class 2: Rock music enthusiasts
   • Class 3: Jazz aficionados
   • Class 4: Classical music connoisseurs
     
2. Select test cases representing each equivalence class:some text
   • Test Case 1: User selects pop genre.
   • Test Case 2: User prefers rock music.
   • Test Case 3: User's favorite genre is jazz.
   • Test Case 4: User enjoys classical music.

Boundary value analysis

Boundary value analysis focuses on testing the boundaries or extreme values of input domains. This technique helps uncover potential errors or vulnerabilities that may occur at the edges of the input space, where the system's behavior may change significantly.

Consider testing a new weather forecasting model. The model predicts daily temperatures based on historical data. Here we more explain everything about boundary value analysis. 

Test Case:

1. Identify input variables and their boundaries:some text
   • Temperature (in degrees Celsius): Min = -40°C, Max = 60°C
2. Select test cases at the boundaries and just beyond:some text
   • Test Case 1: Input temperature = -40°C (Lower Boundary)
   • Test Case 2: Input temperature = 60°C (Upper Boundary)
   • Test Case 3: Input temperature = -41°C (Just below the Lower Boundary)
   • Test Case 4: Input temperature = 61°C (Just above the Upper Boundary)

Decision table testing

Making a table that lists several input scenarios and the associated anticipated outputs for the software is the first step in decision table testing. This aids in locating any possible discrepancies in the system's logic.

Consider a loan approval system that takes factors like income and credit score into account. Here's how to create a decision table:

1. Identify Input Conditions: List every essential variable that is taken into an account (e.g., income, credit score, employment status).
2. Define Possible Values: Indicate the range of values that each component is capable of (for example, income: low, medium, or high).
3. Expected Outputs: For every set of input data, ascertain the anticipated outcome regarding loan approval (approved or rejected).

State transition testing

Testing a system's behavior during its transitions between various states or modes is the main goal of state transition testing. This method is crucial for assessing the consistency and accuracy of the system with complicated state machines, such as chatbots or driverless cars.

Consider testing a traffic management system for a smart city. The system controls traffic signals and adjusts timing based on traffic flow.

Test Case:

1. Define different states and transitions:
   • State 1: Normal Traffic Flow
   • State 2: Heavy Traffic Congestion
   • State 3: Emergency Vehicle Priority
2. Create test scenarios to validate state transitions:
   • Scenario 1: During rush hour, go from normal traffic flow to heavy traffic congestion.
   • Scenario 2: When an ambulance is spotted, switch from heavy traffic congestion to emergency vehicle priority.

Error guessing

Error guessing relies on testers' experience, intuition, and domain knowledge to identify potential errors or vulnerabilities in the system. Testers anticipate possible failure modes and devise test scenarios to uncover hidden defects that may not be captured by formal test techniques.

Domain knowledge in action:

Imagine testing a facial recognition system. While the system might be trained on a vast dataset of faces, the tester might consider:

• How would the system handle images with extreme lighting conditions (very bright or dark)?
• Could the system be fooled by manipulated images (e.g., wearing a mask)?

By applying their understanding of facial recognition technology and potential vulnerabilities, the tester can devise test cases that challenge the system's robustness in unexpected situations.

Strategies for effective error guessing:

Here are some strategies to maximize the effectiveness of error guessing:

• Brainstorm potential failure modes: Consider how the system could malfunction or produce incorrect outputs.
  
• Think like an attacker: Imagine scenarios where someone might try to exploit weaknesses in the system.
  
• Rely on domain expertise: Use your knowledge of the specific domain to identify potential edge cases or limitations.
  
• Document your findings: Clearly document any potential errors or vulnerabilities discovered through error guessing to inform further testing and development efforts.

Gain confidence in your digital systems with Alphabin

Alphabin employs AI-driven tools and techniques to tackle the complexities of software testing, ensuring thorough validation and performance optimization. For more information on how we can enhance the strength of your product, contact us.

Conclusion

To sum up, black box testing techniques are essential for guaranteeing the correctness, robustness, and dependability of your system. Testers can uncover and resolve possible problems by assessing the outward behavior of the applications, which helps to establish confidence in these cutting-edge technologies.

We encourage developers and testers to incorporate the discussed black box testing in software engineering and their testing strategies, as they offer valuable insights into the performance and behavior of your systems.